Business Model: Data as a service

Data-as-a-Service

Data-as-a-Service: Data or data management as a core underlying asset; services are created on top of data that is collected, trained, and labeled

Moving Beyond Insights and Analytics

Much of data strategy and delivery has been constrained and siloed within the Business Intelligence function inside of companies. Data-as-a-service models move beyond merely enhancing data processes and analytics to inform internal decisions and move to create value for end customers outside the organizations.

Data-as-a-Service vs. Artificial Intelligence / Machine Learning

AI and ML are data science and computational processes that can extend to any digital business model. Data-as-a-Service is a distinct business model approach that seeks to place a value on a data process that customers will pay for. 

Business Models in Use

Blockscore | Bloomberg |  Climate Corp (acquired by Monsanto) | Epsilon | Experian | Factual | Intelius | IOTA | LiveRamp | People Lookup | PIPL | Qlik | Sift Science | Socrata | Streamr | ZabraSearch 

Why Customers Like DaaS:

Benefits for Customers

  • Saves Money: No need for expensive infrastructure or data storage systems—customers pay only for what they use.
  • Feeds a Data Chain that Drives Revenue: Enables businesses to integrate high-quality data into their operations, powering insights that uncover new revenue opportunities or re-envision processes.
  • Fast Access to Data: Provides real-time, ready-to-use data that helps businesses make quick decisions.
  • Grows with Your Needs: Customers can scale data usage up or down without the hassle of managing storage.
  • Customizable: Offers tailored datasets that are specific to a business’s needs, saving time and effort.
  • Less Work for IT: The provider handles collecting, cleaning, and managing data, so customers can focus on using it.
  • Fills Critical Data and Data Quality Gaps: Historically, business data was generated internally or through direct customer interactions, stored in structured, manageable formats on in-house servers. Today, most data is external, vast, fast-moving, and varied, originating from consumers, sensors, and machines outside the enterprise. Data-as-a-Service addresses this shift by preparing data for customers—filling gaps, filtering, cleaning, aggregating, and simplifying access. Companies may create and sell proprietary datasets (Factual), provide robust access points (Bloomberg), or enable customers to integrate external data into their own systems (Zephyr Health, Socrata).

Why Companies Like DaaS:

Benefits for Companies

  • Steady Income: Subscriptions and pay-as-you-go models provide reliable, recurring revenue.
  • Easy to Scale: Cloud-based delivery means companies can grow their customer base without major cost increases.
  • Understand Customers Better: Usage data helps companies improve their services and create opportunities to upsell.
  • High Profits Over Time: Once datasets are created, companies can sell them repeatedly at minimal extra cost.
  • Reach More Customers: Cloud delivery allows companies to serve businesses in many different industries and locations.

What do Investors Think of DaaS?

Why Investors May Like DaaS

  • Recurring revenue makes DaaS businesses predictable and scalable.
  • Demand for high-quality, real-time data is growing across industries like healthcare, finance, and marketing.
  • Companies with unique or proprietary data have a strong competitive advantage.

Why Investors May Be Skeptical of DaaS

  • Collecting and maintaining data is expensive and takes time to become profitable.
  • Many startups claim to be pursuing a future data-driven model that never materializes. 
  • There’s a risk of competitors offering similar data at lower prices.
  • Ensuring data privacy and compliance with regulations can be complicated and costly.

DaaS KPIs

  • Recurring Revenue: Tracks income from subscriptions or usage-based payments.
  • Customer Lifetime Value (CLTV): Measures how much revenue a customer generates over time.
  • Data Quality: Assesses the accuracy and relevance of the data customers receive.
  • Churn Rate: Shows how many customers stop subscribing to the service.
  • Usage Metrics: Tracks how much data customers use and how often they access it.
  • Compliance Score: Measures how well the service adheres to data privacy laws like GDPR or CCPA.

Challenges to the DaaS Model

  • Privacy Concerns: Customers worry about how their data is handled and if it’s secure.
  • High Setup Costs: Collecting and cleaning data requires a lot of upfront investment.
  • Standing Out: Standard datasets can be easy for competitors to replicate, making differentiation difficult.
  • Customer Trust: Some businesses hesitate to trust third-party data providers, especially in highly regulated industries.
  • Economic Downturns: Customers may cut back on data subscriptions during tough times.

Strategic Responses to DaaS Challenges

  • Focus on Unique Data: Offer datasets that customers can’t get elsewhere to stand out from competitors.
  • Keep Data High Quality: Regularly update and clean your data so customers can trust it’s accurate and useful.
  • Flexible Pricing: Offer different pricing tiers or pay-as-you-go plans to fit more budgets.
  • Secure and Compliant: Invest in strong security and show customers that you follow data privacy laws.
  • Bundle Services: Pair data with tools like analytics or AI to offer extra value and increase loyalty.

Before You Consider DaaS

  • Where does the solution fall on the data-knowledge-wisdom hierarchy?
  • Test for jobs to be done, level, pain on the pain scale. How much of a priority is this solution?
  • Is your data ready to be used? Do we require additional data from the customer, third parties, or open-source data projects?Is there a true competitive advantage to your hardware; can it be replaced by a SaaS offering
  • What is the culture of data-informed decisions within our customer base?
  • Are our customers data-literate? Can they review Python libraries or dashboards? Do they need to review everything in Powerpoint with an analyst at the ready?
  • What decisions will be affected by our data solution?

Testing the Model

  • What is the total cost of ownership of comparable solutions?
  • Arrange features, services, and benefits into key elements of your offer and have the potential customer arrange the elements of the larger solution in order of priority. Then take away the lesser priority elements until you determine what would make an MVP (minimum viable product).
  • Determine the minimal offering that would be compelling enough to have the customer pay for the offering.
  • Can you design an MVP that has high usage and engagement with a minimal feature set?
  • Is there a user proposition that does not require sign-off from IT or a long buying cycle?

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